The promise of digital healthcare technologies

Frontiers in

Public Health

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The promise of digital healthcare

technologies

Andy Wai Kan Yeung

*, Ali Torkamani

, Atul J. Butte

Benjamin S. Glicksberg

, Björn Schuller

, Blanca Rodriguez

Daniel S. W. Ting

, David Bates

, Eva Schaden

Hanchuan Peng

, Harald Willschke

, Jeroen van der Laak

Josip Car

, Kazem Rahimi

, Leo Anthony Celi

Maciej Banach

, Maria Kletecka-Pulker

, Oliver Kimberger

Roland Eils

, Sheikh Mohammed Shariful Islam

Stephen T. Wong

, Tien Yin Wong

, Wei Gao

Søren Brunak

and Atanas G. Atanasov

Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of

Dentistry, University of Hong Kong, Hong Kong, China,

Ludwig Boltzmann Institute Digital Health and

Patient Safety, Medical University of Vienna, Vienna, Austria,

Department of Integrative Structural and

Computational Biology, Scripps Research Translational Institute, La Jolla, CA, United States,

Bakar

Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA,

United States,

Department of Pediatrics, University of California, San Francisco, San Francisco, CA,

United States,

Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount

Sinai, New York, NY, United States,

Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn

School of Medicine at Mount Sinai, New York, NY, United States,

Department of Computing, Imperial

College London, London, United Kingdom,

Chair of Embedded Intelligence for Health Care and

Wellbeing, University of Augsburg, Augsburg, Germany,

Department of Computer Science, University

of Oxford, Oxford, United Kingdom,

Singapore National Eye Center, Singapore Eye Research Institute,

Singapore, Singapore,

Duke-NUS Medical School, National University of Singapore, Singapore,

Singapore,

Department of General Internal Medicine, Brigham and Women’s Hospital, Harvard Medical

School, Boston, MA, United States,

Department of Anaesthesia, Intensive Care Medicine and Pain

Medicine, Medical University of Vienna, Vienna, Austria,

Institute for Brain and Intelligence, Southeast

University, Nanjing, China,

Department of Pathology, Radboud University Medical Center, Nijmegen,

Netherlands,

Primary Care and Public Health, School of Public Health, Imperial College London,

London, United Kingdom,

Centre for Population Health Sciences, LKC Medicine, Nanyang

Technological University, Singapore, Singapore,

Deep Medicine Nuffield Department of Women’s and

Reproductive Health, University of Oxford, Oxford, United Kingdom,

Institute for Medical Engineering

and Science, Massachusetts Institute of Technology, Cambridge, MA, United States,

Department of

Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States,

Department of

Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States,

Department of

Preventive Cardiology and Lipidology, Medical University of Lodz (MUL), Lodz, Poland,

Department of

Cardiology and Adult Congenital Heart Diseases, Polish Mother’s Memorial Hospital Research Institute

(PMMHRI), Lodz, Poland,

Institute for Ethics and Law in Medicine, University of Vienna, Vienna, Austria,

Digital Health Center, Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin,

Germany,

Institute for Physical Activity and Nutrition, Deakin University, Geelong, VIC, Australia,

Department of Systems Medicine and Bioengineering, Houston Methodist Cancer Center, T. T. and W.

F. Chao Center for BRAIN, Houston Methodist Academic Institute, Houston Methodist Hospital,

Houston, TX, United States,

Departments of Radiology, Pathology and Laboratory Medicine and Brain

and Mind Research Institute, Weill Cornell Medicine, New York, NY, United States,

Tsinghua Medicine,

Tsinghua University, Beijing, China,

Andrew and Peggy Cherng Department of Medical Engineering,

California Institute of Technology, Pasadena, CA, United States,

Novo Nordisk Foundation Center for

Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen,

Denmark,

Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences,

Jastrzebiec, Poland

Digital health technologies have been in use for many years in a wide spectrum of

healthcare scenarios. This narrative review outlines the current use and the future

strategies and significance of digital health technologies in modern healthcare

applications. It covers the current state of the scientific field (delineating major

strengths, limitations, and applications) and envisions the future impact of

relevant emerging key technologies. Furthermore, we attempt to provide

OPEN ACCESS

EDITED BY

Bokolo Anthony Jnr,

Institute for Energy Technology, Norway

REVIEWED BY

Durga R,

The College of Haringey, Enfield and North

East London, United Kingdom

Vathsala Patil,

Manipal Academy of Higher Education, India

Olga Chivilgina,

University of Basel, Switzerland

*CORRESPONDENCE

Andy Wai Kan Yeung

ndyeung@hku.hk

Atanas G. Atanasov

Atanas.Atanasov@dhps.lbg.ac.at

RECEIVED

01 April 2023

ACCEPTED

04 September 2023

PUBLISHED

26 September 2023

CITATION

Yeung AWK, Torkamani A, Butte AJ,

Glicksberg BS, Schuller B, Rodriguez B,

Ting DSW, Bates D, Schaden E, Peng H,

Willschke H, van der Laak J, Car J, Rahimi K,

Celi LA, Banach M, Kletecka-Pulker M,

Kimberger O, Eils R, Islam SMS, Wong ST,

Wong TY, Gao W, Brunak S and

Atanasov AG (2023) The promise of digital

healthcare technologies.

Front. Public Health

11:1196596.

doi: 10.3389/fpubh.2023.1196596

Schuller, Rodriguez, Ting, Bates, Schaden,

Peng, Willschke, van der Laak, Car, Rahimi, Celi,

Banach, Kletecka-Pulker, Kimberger, Eils, Islam,

Wong, Wong, Gao, Brunak and Atanasov. This

is an open-access article distributed under the

terms of the

Creative Commons Attribution

License (CC BY)

. The use, distribution or

reproduction in other forums is permitted,

provided the original author(s) and the

original publication in this journal is cited, in

accordance with accepted academic practice.

No use, distribution or reproduction is

permitted which does not comply with these

terms.

TYPE

Review

PUBLISHED

26 September 2023

DOI

10.3389/fpubh.2023.1196596

Yeung et al.

10.3389/fpubh.2023.1196596

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recommendations for innovative approaches that would accelerate and benefit

the research, translation and utilization of digital health technologies.

KEYWORDS

digital health, biosensors, bioinformatics, telehealth, precision medicine

1. Digital health technologies: a

snapshot

1.1. General significance of digital health

technologies through history until today

According to the World Health Organization (WHO), digital

health technologies can be defined as:

“the field of knowledge and

practice associated with the development and use of digital technologies

to improve health... Digital health expands the concept of eHealth to

include digital consumers, with a wider range of smart and connected

devices. It also encompasses other uses of digital technologies for health

such as the Internet of Things (IoT), advanced computing, big data

analytics, artificial intelligence including machine learning, and

robotics”

(

). Importantly, in the context of digital health

technologies, several terms such as eHealth (electronic health),

telemedicine, and mHealth (mobile health) have been widely used,

unfortunately on some occasions with overlapping meaning,

underlining the necessity of using more precise scientific language

reflecting the subtle differences between such relevant terms (

Along this line, “digital health” has been coined to represent the

broadest term covering the application of digital technologies in the

context of health, and while being rooted in electronic health, this

term also encompasses other adjacent areas such as “big data”

applications, genomics, and artificial intelligence. Further, eHealth

is often referred to as the use of information and communications

technology in support of health, mHealth is viewed as a branch of

eHealth that refers to the use of wireless mobile technologies for

public health (gaining particular momentum with the wide adoption

of smartphones and respective apps), and telemedicine is a term

reflecting the use of electronic communications and information

technologies for remote provision of health care services (

Telemedicine, for example, has been a contemporary recurring

discussion topic in the scientific, government and healthcare

community, especially when the majority of the global population has

experienced various scales of community lockdowns, home quarantines,

and reduced availability of medical services during the COVID-19

global pandemic (

). The COVID-19 pandemic that began in early 2020

accelerated the expansion and implementation of existing and novel

digital health technologies through increasing funding, fast-track policy

approvals, enhanced governmental priorities, new private-public

partnerships, and the pooling and planning and design of various

collaborative research (

). On hindsight, the utilization of telemedicine

should have started as soon as the phone came into use by physicians.

Since its invention in 1876, the telephone has been used as a tool for

delivering healthcare: Alexander Graham Bell’s first recorded telephone

call was for medical help after he spilt sulphuric acid on himself (

In the late 20

century when the healthcare industry began to first

embrace computerization and incorporate information technology, the

initial intentions were focused on streamlining procedures to reduce

manually introduced errors in the workflow. The impact of medical

errors can be minimized by preventing erroneous entries and by

mitigating the risk of adverse events, facilitating a more prompt

response after an adverse event has occurred, and tracking and

providing feedback about any adverse event (

). For example, a

computer-based decision support systems could identify interactions

between different drugs taken by a patient and prevent adverse drug

events (

–

). Meanwhile, a physician computer order entry system

(CPOE) reduced 55% of the non-intercepted serious medication errors

in a hospital located in Boston, the United States (

). Along the chain

of steps in the workflow from diagnosis to medication, digital health

technologies such as electronic documentation, bar coding, and robots/

automated dispensing devices can be helpful in reducing errors (

). More recently, research has focused on digital health technologies

on three main aspects: data storage, management, and transmission;

clinical decision support; and telemedicine (

). However, it is not

clearly evident that these have led to substantially improved clinical

care or improved cost-effectiveness of healthcare services.

Currently, the spectrum of digital health technologies has

expanded and includes not only telemedicine (concept that was

developed before the time of digital technologies, but was markedly

reshaped from the latter), but analysis and utilization of big data,

comprehensive health record digitization, IoT, wireless and mobile

technology/5G, blockchain, artificial intelligence and machine

learning (AI/ML) including deep learning, and wearable monitors

(biosensors). The increasing accessibility of cloud computing and

cloud storage may facilitate more complex diagnostic procedures via

telemedicine, such as bioimage analysis that requires computational

power not locally available (

–

). The near ubiquitous penetration

of mobile phone to vast populations across the globe will likely play

an increasing role in digital health technologies, with this upcoming

research field being coined as mHealth (

). Particularly with the

big data from electronic patient records (or real-world data (

)) that

forms a digital knowledge base, AI analyses can be performed to aid

diagnosis and treatment selection, resulting in an improved clinical

decision support (

) and image-based medical diagnosis (

Compared with traditional healthcare, digital healthcare can

potentially be more precise, less error-prone, and more efficient

[

Table 1

, based on Meskó et al. (

)]. With consideration of the listed

developments, the present work aims to give perspective of the

promise of digital health technologies in healthcare.

1.2. Challenges associated with digital

health technologies

There are major challenges in the implementation of new

technologies, particularly disruptive technologies, in an established

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industry such as healthcare. From the patient perspective, the first

obvious challenge with digital health technologies is the inability to use

the technology or mobile phones due to low digital health literacy or

low access to technology, especially for the older adults and people with

lower digital literacy (

). Second, poor app design may hinder the

implementation or growth of digital health technologies, such as being

one of the barriers to adopting telemedicine besides staff technology

level, resistance to change, cost, and patient age and education level

(

). Without clear advantage and ease of use, physicians may not have

the incentive to implement the technologies or ask patients to use them.

Poor App design may only make the usage less efficient, but a

more severe issue is the lack of rigorous regulations for assurance of

quality/effectiveness or insufficient validation of clinical effectiveness.

This may seriously tarnish the general impression of digital health

technologies by the public/society. For instance, it was found that

mental health apps, totally downloaded more than 2 million times,

provided non-existent or inaccurate suicide crisis helpline phone

numbers, with only 5 out of 69 depression and suicide prevention

apps offering all six evidence-based suicide prevention strategies (

While the introduction of new medication into clinical practice

requires rigorous evaluation and regulation and involves head-to-

head studies, it seems that health apps can be introduced into the

market with less quality assurance. This implied further needs for

improvement at the policy level. For example, the performance of a

dermatology app was tested with biopsy-proven melanoma pictures,

and it was found that the app could only label 11% of the pictures as

high risk and another 88% as medium risk (

). This situation seemed

to be gradually improving as technology has become more matured.

A more recent study that evaluated 8 symptom assessment apps on

their “

breadth of condition coverage, accuracy of suggested conditions

and appropriateness of urgency advice

” has found that some apps had

comparable performance with general practitioners (GPs) in these

aspects, but still, none could outperform GPs (

). Unfortunately, the

mainstream reviews of mHealth apps have been hugely based on

personal experiences, with few evidence-based, unbiased evaluations

of clinical performance and data security (

). As such, the accuracy

or usefulness of the apps should be further verified before their

release for clinical use (

). One crucial aspect that should perhaps

be highlighted was that there seemed to be no study of the clinical

risks and benefits of the apps involving real-world consumer use (

Among other frequently recognized challenges such as insufficient

technology support, high cost, privacy and security concerns, and

compatibility with digital solutions established at hospital/ambulatory

systems, data ownership uncertainty stands out as a particularly

important present and future issue. There is always a “fight” between

big companies owning the data and charging for every analysis and

access, versus patients/healthcare professionals owning it and retaining

their rights to use it in whatever ways are deemed to yield better care.

In this context, a recent literature review has summarized the following

points (

): (1) there was wide concern about the security of mHealth

data storage and transmission; (2) aggregated data previously

considered “de-identified” could actually be re-identifiable; (3) there

might be a lack of consumer-informed consent due to the absence of a

lengthy; and (4) improved access control should be advocated. Some

of the ethical considerations are specifically elaborated below:

1.2.1. Privacy and security

Digital health technologies collect and store large amounts of

personal health information. There is a risk that this information

could be accessed or breached by unauthorized personnel, leading to

privacy issues, potential identity theft, and other forms of damage. It

is critical to ensure that digital health technologies comply with data

privacy regulations, have robust security measures, and provide

patients with adequate control over their data privacy settings.

1.2.2. Informed consent

Patients should have the right to be informed and to participate

in their healthcare decisions. The use of digital health technologies

should not omit or dismiss the importance of obtaining informed

consent from the patients, especially if they are unaware of what data

would be collected and how the data would be distributed. Also, it is

important to allow patients to opt-out of data collection and sharing

in an explicit and straightforward manner.

1.2.3. Algorithmic bias

Digital health technologies rely on algorithms to analyze health data

and provide recommendations/decisions regarding patient care. If the

algorithms are biased, unfair or discriminatory treatment of patients

might happen. Hence, it is crucial to validate the algorithms used in

digital health technologies to ensure they are unbiased and do not

perpetuate existing inequalities before releasing them into the market.

1.2.4. Equity and access

Digital health technologies have the potential to improve

healthcare access and equity by providing remote care. However,

patients without access to the technology or digital literacy skills will

still be excluded. Healthcare providers should ensure that digital

health technologies are accessible to all patients, regardless of their

socioeconomic status or location.

TABLE 1

Differences between traditional and digital healthcare.

Traditional healthcare

Digital healthcare

Direct patient-physician relationships

Patient-machine-physician interface

Standardized care based on physician experience and standard clinical workflow:

Symptoms, clinical signs, ancillary medical tests, diagnosis, and treatment plan

Individualized care, precision medicine, with non-traditional workflow: Mass

screening, early preclinical or asymptomatic diagnosis, diagnosis based on

probability, predictive technology, and decision support for physicians

Point of care delivery or examination is at the clinic or lab

Point of care delivery or examination may vary as long as patient is present

Data owned by the institutions/hospitals

Data owned and shared by multiple stakeholders, including the patient

Physician as the central player who makes diagnosis, and prescribes treatment plan

Physician as a consultant, guide or collaborator with the patient’s active contribution

in the decision making

The table is based on Meskó et al. (

), with adaptations from the authors.

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1.2.5. Professional integrity

The use of digital health technologies may affect healthcare

professionals’ professional integrity, as they may rely on technology to

make decisions rather than their clinical judgment. Healthcare

professionals should be adequately trained in the use of digital health

technologies and they should be reminded of their responsibility and

liability in providing healthcare.

There are also challenges about the existence of multiple

competing technologies in the same area that are often not

compatible, and thus data cannot be easily interchanged or

transferred between competing platforms (interoperability issue).

Data heterogeneity also creates difficulties for the analysis and the

interpretation. This was the case with the contact-tracing apps and

electronic vaccination records developed during the COVID-19

pandemic: the so-called “format wars” (

). This issue particularly

affects more complex multimedia data types including patient

videos, audios, digital pathology, IoT, social media, and further.

For example, the analysis of neuroimaging data by neuroscientists

often begins with data conversion from the Digital Imaging and

Communications in Medicine (DICOM) format to the

Neuroimaging Informatics Technology Initiative (NIfTI) format,

which itself might require some expertise (

). It is because the

DICOM specification is complex and allows for variability among

different manufacturers to embed customized data into the odd

column of DICOM headers (

). Therefore, some researchers

advocated that any shared data observe the principles of being

findable, accessible, interoperable, and reusable (FAIR) (

). A

related problem is that if accurate linkability between data types at

the level of individuals could be applied. Not all countries have

ubiquitously used personal identification numbers that in principle

can make data types linkable even if formatting issues may make

linking cumbersome. In the Nordic countries for example, as well

as many other places, health data can be linked to socio-economic

data unproblematically as the same identification number is used

across private organizations, state agencies, municipalities and

other data owners. In Denmark the national identification number

was for example implemented as early as 1968, making data

linkable way back in time (

). To manipulate such a large

amount of personal data, secured cloud computing and storage

seemed to be the preferred choice.

With regard to the costs involved in implementing digital health

technologies, sometimes a lack of cost-effectiveness was established.

A recent systematic review (

) on cost-effectiveness studies of digital

health technologies found that 2 out of 17 studies on video-

conferencing systems reported a lack of cost-effectiveness, which

could be attributed to reasons such as upfront training costs and

resource-intensive intervention (

Meanwhile, the use of digital health technologies might not always

improve clinical care. For instance, digital health technologies could

not reduce adverse outcomes for pregnant women with gestational

diabetes during delivery, such as pre-eclampsia/eclampsia or the need

for use of medication (

Challenges associated with the application of artificial intelligence/

machine learning (AI/ML) are more specific, such as explainability,

trustability, fairness, and personalization. For instance, a survey found

that patients preferred to have an AI acting as a physician assistant

rather than the main physician, implying that there were trust issues

to be overcome (

). Meanwhile, the type of training data should also

determine the application of the trained model, such as models based

on observational data would better refine existing practices instead of

discovering new treatment options (

A major limitation of many digital health technologies is that they

require significant up-front investments, including the purchase of

expensive equipment or systems. Thus, these technologies cannot

be readily afforded by poorer countries or communities, rendering them

not applicable to a global scale. A recent systematic review summarized

that the major barriers in poorer countries included infrastructure,

equipment, internet, electricity and the digital health technologies

themselves, and they could be considered in three levels: project design

and implementation factors; factors within the organizational settings;

and factors in the broader community environment (

). Key challenges

are summarized into technical and non-technical categories and

approaches to overcome them are also listed in

Table 2

1.3. Potential of digital health technologies

from the patient perspective

Despite the multiple challenges encountered in inventing and

implementing digital health technologies, there are simultaneously

TABLE 2

Challenges and recommendations to overcome them.

Challenges

Possible approaches to overcome

Technical

Data structure and heterogeneity (interoperability)

Unify data format, security and sharing requirements

Digital technology infrastructure

Cloud computing and storage; use of blockchain for secured and decentralized data storage and transport

Non-technical (4 Ps)

Patient (lack of acceptance, privacy issue, lack of

motivation, fear of technology, etc.)

More “how to use” quick guides and ready-to-help staff; more patient involvement in the design; more support to

caregivers; encourage promotion from the patient’s attending physicians

Physician (resistance, lack of incentives, fear of losing

jobs, changing roles, etc.)

System overhaul and accredited points for continuous professional development schemes; establish clarity in

regulation and standardization

Public/society (ethics, acceptance, public education etc.)

Promotional campaigns led by celebrities; evaluate and demonstrate evidence of cost-effectiveness

Policy (ethics, financial, regulatory, especially in less

resourceful countries)

Lobbying and public-private partnerships; establish clear legal framework regarding reimbursement schemes

and data transparency; provide subsidy to cover high start-up costs or incentivize the use

The table is based on Ambrosino et al. (

), Frederix et al. (

); Shimbo et al. (

); Bhyat et al. (

); Naik et al. (

); Murthy et al. (

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significant potential associated with the application of digital health

technologies. One major direction of high promise is the facilitation of

personalized medicine. Personalized medicine can be defined as “

tailored

disease prevention and treatment for individual variability (

e.g.

, genetic

and lifestyle differences among patients) ... [and its goal is] to match the

right treatments at the right dosages for each individual patient at the right

time

” (

). It requires a precise analysis of a patient’s health parameters

such as vital signs, blood test results, bioimage interpretations, and more.

Complex decision models should be built on established large patient

databases. Promising results have been for example reported from

studies with animal and human data that used calibrated populations of

models to predict and explain intersubject variability in cardiac cellular

electrophysiology and atrial electrophysiology (

). By participating

in personalized omics profiling projects, patients could also

be encouraged to implement diet and exercise changes, with the collected

data being used to build prediction models to predict personalized

physiological responses such as insulin resistance (

). Along the same

line, combining existing data from an electronic patient record system

together with genomic data could analyze the fine-scale population

structure that impacted genetic risk predictions (

Another untapped potential of importance is cognitive automation

using virtual Avatar doctors to alleviate the shortage of medical

specialists in underserved regions and to enable efficient access to care.

Currently, it is possible to set up and operate an augmented virtual

doctor office through online multimedia platforms such as Second Life

(

). With non-invasive sensors and deep neural networks, AI could

build a virtual doctor that was able to autonomously interact with a

patient via speech recognition and speech synthesis system (

). Such

technology is particularly beneficial to remote and rural areas, where

primary healthcare is usually very limited due to low population density.

As a proof-of-concept, the system could predict type 2 diabetes mellitus.

Digital health technologies could also facilitate access to health

services, more direct communication with the healthcare provider,

and full access to information storage and sharing to enable better

follow-up and clinical decision making (

), as well as promotion

of patient empowerment, better patient adherence and compliance,

circumventing geographical barriers (

Table 3

). One hypothetical

advantage of a virtual doctor is that it can improve access to

healthcare by patients with limited mobility, such as patients with

physical disabilities and frail older adults (

). However, it is

unclear if they already had the experience or capability to use the

technology of a virtual doctor, or if there could be caretakers readily

available to teach them or use the technology with them together.

1.4. Representative success stories

involving digital health technologies

As with the adoption of new technologies, successful stories

provide deep insights. One common digital health technology is the

bar code technology, which is now frequently implemented in

pharmacy. Drug dispensing in a clinical or hospital setting involves

many steps that may go wrong especially in a hospital setting, where

staff members need to dispense drugs to multiple patients on a

regular and timely basis, which may further promote errors to

occur. The use of bar codes may insert verification steps in the chain

of workflow to ensure that errors will not accumulate and pass to

the subsequent steps. For instance, studies at a hospital pharmacy

of a 735-bed tertiary care academic medical center found that, when

staff were required to scan all doses of medications during

dispensing, the incidence of dispensing errors had a significant

93–96% relative reduction, compared to only visual inspection on

retrieval (

); on a related note, the rate of potential adverse drug

events (errors determined to be potentially harmful to patients)

significantly dropped from 3.1 to 1.6% (

). Meanwhile, the

traditional rectangular-shaped bar code has been evolving into the

square-shaped Quick Response code (QR code) that can be scanned

by mobile phones. The QR code has been used by many digital

contact-tracing apps during the COVID-19 pandemic (

Another common digital health technology that is already

proudly used by consumers is the wearable sensor, with a notable

example being the Fitbit family of smartwatches. A study reported

that steps, heart rate, energy expenditure, and sleep data collected

by Fitbit could detect adults at a high risk of depression with

around 80% accuracy, sensitivity, and specificity (

). Another

study reported that Fitbit could reliably detect sleep–wake states

and sleep stage composition relative to polysomnography,

particularly in the estimation of rapid-eye-movement (REM) sleep

but not N3 sleep (“deep sleep”) (

). Another use of wearables is

tag-based real-time locating system (RTLS), which was used for

contact tracing in hospital settings during the COVID-19

pandemic (

TABLE 3

Summary of the role of digital health technologies in promoting patient engagement and empowerment.

Role

Description

Access to health

information

Patients can have access to their health information, including medical records, test results, and self-management tools,

via

digital health

technologies. This information empowers patients to make relevant informed decisions more actively together with their healthcare providers.

Improved

communication

Digital health technologies facilitate communication between patients and healthcare providers, enabling patients to ask questions, provide

feedback, and receive advice and guidance apart from face-to-face consultation sessions. This increased communication may encourage patients

to be more aware of their own daily health condition, and have greater satisfaction.

Personalized care

Healthcare providers may deliver personalized care with tailored treatment plans according to the health metrics collected

via

digital health

technologies such as wearables. The personalized approach may improve patient engagement and adherence, and increase treatment efficacy.

Remote monitoring

Wearables and remote monitoring systems enable patients to monitor their health at home and share data with their healthcare providers.

Healthcare providers can therefore detect and address health issues more pre-emptively, leading to better prognosis and reduced treatment costs.

Self-management

Digital health technologies are tools and resources for patients to manage their health more independently, such as medication reminders,

exercise trackers, and nutrition apps. They can increase patient engagement and self-efficacy, leading to improved health outcomes.

The table is based on Lupton (

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During the COVID-19 pandemic, digital health technologies have

gained unprecedented importance as they could help in monitoring,

surveillance, detection and prevention of COVID-19 directly and

indirectly (

). The analysis of clinical data of COVID-19 patients

with AI/federated learning could effectively predict their clinical

outcomes such as the necessity to use mechanical ventilation or death

at 24

h (

). However, when we look back retrospectively, it should

be noted that many prediction model studies were poorly reported

with high risk of bias (e.g., potential inclusion of mislabeled data or

data from unknown sources) such that their predictive performances

might be over-optimistic (

) or of limited potential clinical use (

These examples of successful implementation of digital health

technologies are summarized in

Table 4

2. Digital health technologies: an

outlook

2.1. Recent technological and scientific

developments expected to impact digital

health technologies

In the early 2010s, mHealth technologies were anticipated to

transform healthcare in the foreseeable future (

). However, the

maturation of AI/ML should also not be overlooked. Even more

recently, deep learning systems can be utilized for disease detection,

such as detecting diabetic retinopathy from retinal images (

) or

papilledema from ocular fundus photos (

). Deep learning is also

popularly tested for histopathology (

). Under a competition

setting, the best AI algorithms developed could detect and grade

prostate cancer on biopsy images, with an agreement reaching 0.86

with expert uropathologists (

). In another competition, a deep

learning algorithm even outperformed a panel of 11 pathologists in

detecting lymph node metastases on tissue section images from

women with breast cancer (

). Besides disease detection, AI also

showed more reliable treatment strategies for sepsis in intensive

care (

). In addition to a single disease entity focus, AI/ML can

also be applied to preventive medicine with supplied big data

consisted of longitudinal multi-omics data [such as genomics (

)],

clinical test results and biomarker analyses acquired in a large

cohort (

). Generative deep learning systems may also be used to

predict how a drug will impact omics data at the individual patient

level, making it feasible to make upfront thought experiments on

drug alternatives rather than testing them on a patient sequentially

as it normally is done (

One of the more recently developed applications that continue

rapidly developing are the wearable sensors. Currently, even

nanomaterial-enabled wearable sensors have been developed to record

various signals belonging to a patient, such as electrophysiological

signals (electrocardiography and electromyography), skin

temperature, body joint movements, electrochemistry of sweat; or

signals belonging to her/his surroundings, such as environmental

humidity, ultraviolet level, and visibility, and such sensors have a vast

potential to yield individualized health-related data (

Important non-wearable sensors include the radio-frequency

identification (RFID) systems that are also commonly used in hospital

settings to track the location of volunteering staff, patient beds,

wheelchairs, and expensive equipment such as special radiology scopes

(

). The active RFID tags, also known as the “beacons,” are used to track

the real-time location of staff and assets; whereas the passive RFID tags

are used to identify patients and facilitate user access to patient data (

Gamification is another digital health-related application that can

improve patient compliance. Digital technologies, such as virtual reality,

can be incorporated into serious games which could bring about

significant improvements in attention and memory functions during

neuropsychological rehabilitation of stroke patients (

). In alleviating

depression, serious games can be classified into exergames (games that

make patients exercise) or computerized cognitive behavioral therapy

(CBT) games. A recent meta-analysis showed that involving in

exergames or computerized CBT games led to significantly less severe

depressive symptoms compared to no intervention, with no significant

difference between exergames and conventional exercises (

Regardless of the medical conditions targeted, there is always a

need for interdisciplinary collaborations to effectively harness the

potential of digital health technologies (

2.2. Recent targets and trends in digital

health technologies

There is a whole spectrum of AI/ML technology that can

be applied in a clinical environment. One example is to use AI/ML to

such as to screen electrocardiograms (ECGs) to identify abnormal

heart rhythms and facilitate healthcare decision making (

It has been estimated that currently every individual has 6–7 mobile

devices connected to the internet, rendering IoT and even the Internet

of Medical Things (IoMT) more practical than before (

). For instance,

IoMT sensors could be used to observe the behavior of a person at risk

of early dementia by collecting patient data and comparing it with

expected behavior according to the existing database (

). Wearable

TABLE 4

Examples of successful implementation of digital health technologies.

Study

Healthcare setting

Targeted outcome

Benefit

Poon et al. (

)

A 735-bed tertiary care academic medical

center

Drug dispensing error

Scan all doses of medications during dispensing could

↓

93–96% error relative to visual check

Poon et al. (

)

A 735-bed tertiary care academic medical

center

Potential adverse drug events

The use of bar-code system caused adverse event rate to drop

from 3.1 to 1.6%

Huang et al. (

)

A COVID-19 screening and treatment center

Contract tracing with COVID-19

patients

The use of RTLS tags had higher sensitivity than smartphone

contact tracing app (95.3% vs. 6.5%)

Dayan et al. (

)

20 institutions/hospitals that screened for

COVID-19 patients

Oxygen requirements of symptomatic

patients with COVID-19

Effectively predicted the clinical outcomes, e.g., the necessity

to use mechanical ventilation or death at 24

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sensors, sensors installed at home, and the wireless sensor networks

altogether could form a comprehensive data collection inventory that

monitors the disease progression of a dementia patient by noticing

abnormality both at home and outside home (

). These examples

illustrate the large amount of data that might be collected from the field

sensors (edge level) and its potential transfer to the cloud (cloud level)

for high performance computing tasks and data storage. The

generalization of the 5G network may provide improved speed, reliability,

energy efficiency, and mobility for such systems (

). However, many

factors might still hinder the prompt response or safety of such systems,

such as security issues, cloud space allocation, and internet speed. As

such, it was proposed that some AI algorithms could be introduced in

the edge level or in the fog level (between the edge and the cloud, where

the local computers and servers gather data and perform local processing

and storage), so as to compensate for the shortcomings of the cloud (

With more and more AI algorithms being implemented for

healthcare use, it is important to plan for algorithmic stewardship,

which monitors the ongoing clinical use and performance of AI

algorithms and ensures they are safe to be used (

). Apart from the

application side, the development side should also establish a reference

standard for the design, execution, and reporting of AI-related studies

such as those assessing the diagnostic accuracy of AI (

). It

should be noted that in 2022 there were nearly 150 clinical trials on

FDA-regulated digital therapeutics so that it is timely to increase the

transparency in their reporting (

One of the most recent trends in the COVID-19 pandemic era

was the development of robotics in healthcare to minimize direct

human contacts to lower the risk of COVID-19 transmission. Some

notable directions are listed in

Table 5

2.3. Advocation of digital health

technologies by the World Health

Organization

With the fast growing number of digital health products such as

AI-driven solutions and health apps, the role of regulatory bodies

becomes even more paramount than before. A recent article

summarized the regulatory approaches from nine countries on health

app policy (

). In brief, some countries have already established their

regulatory framework. For example, Singapore stipulated that apps

must be approved by the Health Sciences Authority prior to their

release for use, and are regulated by laws and non-legally binding

guidelines. Meanwhile in the United States, apps that were classified as

medical devices and of moderate or high risk should be approved and

regulated by the Food and Drug Administration (FDA).

Table 6

shows

some examples of regulatory and ethical considerations related to the

use of digital health technologies in different countries or regions.

However, many health apps were not considered to have met the

abovementioned criteria to be regulated. Taking into consideration that

relevant regulatory frameworks are still to be fully established and the

application of digital health technologies at international scale is

complicated by the diversity of legislations and approaches by different

countries, guidance by international health bodies such as the WHO

can be of great value. Overall, regulatory frameworks and policies

should prioritize patient safety, data privacy and security,

interoperability, ethics, clinical validation, patient-centered approach,

and regulatory harmonization. The integration of digital health

technologies into the existing healthcare system requires input from

various stakeholders (

Figure 1

TABLE 5

Examples of robot use in the COVID-19 pandemic era.

Robot type

Details

Delivery robot

Deliver foods and medicines to patients with COVID-19 in quarantine zone, or to customers in restaurants and older adults homes

Screening robot

Conduct swab tests for mass screening of the public regarding COVID-19, identify individuals with high temperature in the crowd

Surgery robot

Assist or perform surgeries in operation theaters

Disinfection robot

Clean and disinfect places in hospitals, restaurants and shopping malls

Public health robot

Promote health awareness, distribute masks and hand sanitizers

Social robot

Communicate with patients in quarantine, enable face calls with family members in other locations, serve as staff in the kiosk

The table is based on Wang and Wang (

) and Naik et al. (

TABLE 6

Examples of regulatory and ethical considerations related to the use of digital health technologies in different countries or regions.

Country/Region

Regulatory considerations

Ethical considerations

Reference

United States

FDA regulations apply to digital health technologies

intended for medical use

Protect patient safety, privacy concerns on patient data,

sharing regulatory duties with developers to balance between

regulation and space for innovation

(

)

European Union

The CE marking is required for certain digital health

technologies, and the General Data Protection Regulation

(GDPR) applies to data privacy

Balance the protection of individual privacy and the

promotion of growing European data economy

(

100

101

)

Canada

Health Canada regulates digital health technologies that

meet the definition of medical devices

Ensure patient safety in the use of digital health technologies,

to reduce barriers to market entry, stimulate innovation, and

encourage adherence

(

102

)

Australia

The Therapeutic Goods Administration (TGA) regulates

digital health technologies that meet the definition of

medical devices

Protect data privacy, and protect patient safety

(

103

)

China

The National Medical Products Administration (NMPA)

regulates digital health technologies that meet the definition

of medical devices

Foster patient safety and device reliability

(

104

)

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FIGURE 2

Schematic diagram of GP and doctor training by digital health/AI/ML.

TABLE 7

Summary of key features, advantages, limitations of different digital health technologies discussed in this review.

Technology

Key features

Advantages

Limitations

Telemedicine

Video consultations with healthcare

providers

↑

Access to professional care,

↑

convenience,

↓

travel time and costs

Inability to conduct physical exams, potential for

technical difficulties especially on the patient side

Wearables

Devices worn on the body to track

health metrics

Continuous data collection and

monitoring,

↑

patient awareness

Questionable accuracy in some metrics/devices

AI-based diagnostics

Use of artificial intelligence for data

analysis and diagnosis

↑

Efficiency,

↓

costs, No human error

Current AI models may have limited ability to handle

complex cases, potential errors/bugs in algorithms

Mobile health apps (mHealth)

Smartphone apps for tracking health

metrics and managing conditions

↑

Patient engagement and self-

management,

↑

access to health

information

Limited accuracy and reliability in some metrics,

potential for data privacy concerns

Readers can refer to the respective parts of the main text for specific literature references.

The WHO has formed a Global Strategy on Digital Health

aimed to bring together the global digital health community to get

involved in the digital transformation of health (

105

). The WHO

has also published nine recommendations on mHealth interventions

(

106

). To broaden the reach of public health messages, the WHO

has created a Health Alert Chatbot available through WhatsApp,

Facebook Messenger, and Viber to provide information on safety

measures for COVID-19, disease prevention, symptoms, and short-

term and long-term effects (

107

). The WHO also released two

mobile apps, the WHO Academy: COVID-19 Learning App and the

WHO Info App, to provide up-to-date information for clinicians

and patients/healthcare consumers, respectively (

108

). A physical

9-storey WHO Academy hub is currently under construction in

Lyon, France. It is expected to be opened in 2024, and will offer

spaces for in-person and distance learning, which include a health

emergencies simulation center and customized distance and hybrid-

learning classrooms (

109

2.4. Advanced digital health technologies

in clinical trials

Digital technologies can facilitate clinical trials. A recent trial

demonstrated real-time perspiration analysis with multiple simultaneous

measurement of sweat metabolites (glucose and lactate) and electrolytes

(sodium and potassium) with skin temperature (

110

). Plastic-based skin

sensors were incorporated in a wristband or forehead patch,

accompanied with an Android app (

110

). Meanwhile, another trial

found that smartwatch and activity tracker data, together with self-

reported symptoms and diagnostic testing results, had superior

performance to detect COVID-19 infection among symptomatic

individuals compared to considering symptoms alone (

111

). Popular

smartwatches such as by Garmin and the Apple Watch were frequently

involved in clinical trials. In one trial, the use of Garmin together with a

behavioral feedback and goal-setting session and 5 telephone-delivered

health coaching sessions significantly reduced both total sitting time and

prolonged bouts of sitting among breast cancer survivors, compared to

no intervention (

112

). Concurrently, a trial on the Apple Watch found

that among participants who received notification of an irregular pulse,

34% had atrial fibrillation on subsequent ECG examination and 84% of

notifications were concordant with atrial fibrillation, implying the

usefulness of the app for early detection (

113

Meanwhile, some clinical trials showed that digital health

technologies were not helpful. For instance, in a trial of 850 patients

with heart failure, the positive effects of a 9-week program of hybrid

comprehensive telerehabilitation did not increase the percentage of

days alive and out of the hospital, and did not reduce mortality and

hospitalization over a follow-up period of 14 to 26

months (

114

). An

early trial of 3,230 patients with diabetes found that the addition of

telehealth (remote exchange of data between patient and healthcare

FIGURE 1

The integration of digital health technologies into the existing

healthcare system requires input from various stakeholders. Partially

based on Naik et al. (

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providers) reduced the mortality rate at 12

month follow-up, but did

not improve the mean number of emergency admissions per patient

after adjusting for baseline characteristics (

115

116

2.5. GP and doctor training by digital

health/AI/ML

Digital health technologies not only could benefit patients but also

might enhance the training of GPs and healthcare professionals. For

example, an e-learning environment could store videotaped trainees’

information-giving sessions, and enable feedback on the sessions from

peers, communication experts and patients, so that ultimately the

polished skills could be transferred to daily clinical practice (

117

Some researchers even compared the combination of digital health

technology and AI/ML with general automation technology in

aviation, so that a future physician’s role would become a supervisor of

patient healing by interacting with AI/ML or ensuring the accuracy of

the decision-making by AI/ML (

118

). However, instead of being idle,

the future physicians should be able to spend more time on the patient-

doctor relationship, establish rapport, and care for the psychological/

mental needs of the ill patients (

118

). On the other hand, GPs and

healthcare professionals need to learn computer science skills to master

AI/ML programs and fully utilize their potentials. There are many

training programs on the market with diverse duration and content

depth. From a recent report on 100 AI training programs for

radiologists, most of the programs were found to be short, stand-alone

sessions; focused on the basic concepts of AI; mainly covered medical,

technical aspects but not managerial, legal and ethical topics; and

offered in passive mode (no hands-on) (

119

). Perhaps future AI/ML

education should start at the undergraduate level in healthcare

education programs, so that the students could build up their AI/ML

knowledge with medical knowledge in a more coherent way (

120

–

122

Figure 2

is a schematic diagram that summarizes this section.

3. Conclusion

This review has covered the current state of the scientific field of

digital healthcare technologies, the promise of the technologies, the

current limitations and challenges, and the potential for applications

in different healthcare scenarios. A summary of the key features,

advantages, limitations of different digital health technologies is

presented in

Table 7

. The interconnections between these components

are now illustrated in

Figure 3

. It is clear that the future of medicine

and healthcare will involve increasing adoption of various kinds of

digital technologies. Some, such as the use of cloud computing that

incorporates AI/ML analytics are especially promising. Many routine

aspects of the healthcare pathway will be automated (e.g., verified with

bar codes/QR codes/wireless RFID to reduce manual errors), and the

diagnostic and management aspects of clinical care will likely be more

personalized (with consideration of multi-omics data and real-time

health surveillance data with wearable sensors). The implementation

of digital health technology in healthcare will result in different

clinical workflows and would need to implement more Quality

Control/Standard Operating Procedures to maintain the integrity and

consistency of digital apps over time and over the total of costs of

ownership. Along this line, algorithmic stewardship should

be implemented with the consideration of total ownership

perspectives, and validation should be applied more than once, as

demographics and care procedures change over time. Importantly,

innovative approaches that would accelerate the research, translation

and utilization of digital health technologies are critically needed.

Moreover, future will witness more digital health workflows going

beyond hospital walls and inpatient care, and this would also require

proper training of caregivers and patients in using digital health apps.

Certainly, digital health comes with challenges. Various stakeholders

in the healthcare sector may be hesitant and concerned with the

changes. Some changes and digital health implementations may

be reverted or stopped once the health and manpower concerns

associated with COVID-19 are over. On the other hand, the use of

semi-or fully automated robots to perform various tasks such as food

and medication delivery to patients and disinfection of hospital seem

to have a good reception and may continue in the future. Healthcare

providers may have many concerns, such as being replaced by digital

health technologies, changes in their duties, and being unable to adapt

to the new working duties and environment. Patients may also fear of

having worse quality of care and less direct communication with care

providers. However, advances in digital health technologies seem to

FIGURE 3

The interconnections between various components in digital health

technologies.

FIGURE 4

The future trend of digital health technologies with virtual reality,

genomics, or blockchain.

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be unavoidable and their usage will spread to less resourceful countries

once they are produced and implemented in mass scale with reduced

costs. Virtual reality, genomics, and blockchain may play important

roles in the future of digital health technologies and require more

research in clinical settings with different patient groups (

Figure 4

Author contributions

AY and AA conceived, designed, and coordinated the writing of

the whole manuscript. All the authors contributed to critically revise

and approve the final version of this manuscript.

Funding

SB would like to thank the Novo Nordisk Foundation (grants

NNF17OC0027594 and NNF14CC0001).

Conflict of interest

Outside the submitted work SB reports ownerships in Intomics

A/S, Hoba Therapeutics Aps, Novo Nordisk A/S, Lundbeck A/S,

ALK abello A/S and managing board memberships in Proscion A/S

and Intomics A/S.

The remaining authors declare that the research was conducted

in the absence of any commercial or financial relationships that

could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors

and do not necessarily represent those of their affiliated organizations,

or those of the publisher, the editors and the reviewers. Any product

that may be evaluated in this article, or claim that may be made by its

manufacturer, is not guaranteed or endorsed by the publisher.

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